Meta Learning with Relational Information for Short Sequences
Yujia Xie · Haoming Jiang · Feng Liu · Tuo Zhao · Hongyuan Zha

Tue Dec 10th 05:30 -- 07:30 PM @ East Exhibition Hall B + C #44

This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta Learning method for Short Sequences) for learning heterogeneous point process models from a collection of short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptively learning for each individual sequence. We further propose an efficient stochastic variational meta-EM algorithm, which can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events.

Author Information

Yujia Xie (Georgia Institute of Technology)
Haoming Jiang (Georgia Institute of Technology)
Feng Liu (Florida Atlantic University)
Tuo Zhao (Georgia Tech)
Hongyuan Zha (Georgia Tech)

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